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Description

Knowledge discovery is the process of developing and applying strategies to
discover useful and ideally all previously unknown knowledge from
historical or
real-time data. Applied to biological and life sciences data, knowledge
discovery
processes will help in various research and development activities, such
as (i)
studying data quality for possible anomalous or questionable expressions of
certain genes or experiments, (ii) identifying relationships between genes
and
their functions based on time-series or other high throughput genomics
profiles,
(iii) investigating gene responses to treatments under various experimental
conditions such as in-vitro or in-vivo studies, and (iv) discovering
models for
accurate diagnosis/classifications based on expression profiles among two or
more classes.

This presentation consists of three parts. In part one, we provide an
overview of
knowledge discovery focusing on bioinformatics domain and describe the
BioMine project where we share our experiences on initiating and managing a
data mining project involving several groups. In part two of this talk, we
describe
a few of our case studies using some existing or newly developed methods.
These are all cases in which real genomics data sets (obtained from public or
private sources) have been used for tasks such as gene function
identification
and gene response analysis. In the last part of this talk, we will describe
complexities and challenges in dealing with real data, demonstrate important
areas that need to be carefully understood in a typical data mining
application,
and share some of our experiences gained over the past 7 years.

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